Genotyping arrays based on single nucleotide polymorphisms (SNPs) provide a low-cost, high-throughput platform. The development of a SNP array that fully reflects the genetic diversity of maize (Zea mays L.) germplasm and is applicable to molecular breeding programs is desirable. In this study, we developed a MaizeGerm50K array comprising 50,852 SNPs selected from the resequencing data of 1604 maize inbred lines and other markers. A genome-wide association study using a landrace panel genotyped with the array permitted mapping of several known genes. We also verified a candidate gene, RNA-binding motif protein 24-like 1 (ZmRBM24L1), delaying flowering through overexpression lines. Genomic selection for yield and agronomic traits showed high prediction accuracy. The MaizeGerm50K array is thus a valuable genomic tool for maize genetic studies and breeding.

Maize (Zea mays L.) is a globally significant crop that plays a crucial role in feeding the world’s growing population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) was employed to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding trials. High-density genetic maps were constructed, and plant height was assessed at both single-plant and row scales across multiple developmental stages and genetic backgrounds. The findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with R² values of 0.67 vs. 0.56 and RMSE values of 0.12 m vs. 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F₁DH and F₂DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. This study successfully identified and validated QTLs associated with plant height, thereby revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.

The cell wall composition and structure of the maize stalk directly affects its digestibility and in turn its feed value. Previous studies of stem quality have focused mostly on common maize germplasm, and few studies have focused on high-oil cultivars with high grain and straw quality. Investigation of the genetic basis of cell wall composition and digestibility of maize stalk using high-oil maize is desirable for improving maize forage quality. In the present study, a high-oil inbred line (By804) was crossed as male parent with the maize inbred line B73 to construct a population of 188 recombinant inbred lines (RILs). The phenotypes of six cell-wall-related traits were recorded, and QTL analysis was performed with a genetic map constructed with SNP markers. All traits were significantly correlated with one another and showed high broad-sense heritability. Of 20 QTLs mapped, the QTL associated with each trait explained 10.0%–41.1% of phenotypic variation. Approximately half of the QTL each explained over 10% of the phenotypic variation. These results provide a theoretical basis for improving maize forage quality by marker-assisted selection.